I am trying to train a image classifier that can classify a few categories like “human, animal, cars” , but there is also a category called “others” , which means when the object is not in any of “human, animal, cars”, it should be classified as “others”. It is used in web-camera in real-life, so the “others” includes background, or everything else.
The problem is we can not train good enough for “others” category. What I am doing now, is just like train a normal single-label multi-classification problem (last layer is softmax) with good training/validation results. But when used in the webcam, sometimes the background is recognized as human or car.
Should I train it like a multi-label problem, where the last layer is sigmoid. so we have confidence score for each class?
Or any other ideas.
Thanks a lot.